Method and system of a machine learning model for detection of physical dividers

ABSTRACT

A method is provided for prediction of a physical divider on a road. The method comprises retrieving vehicular sensor data, map data, or a combination thereof captured over a predefined period of time for at least one segment of the road. The method further comprises aggregating the respective vehicular sensor data, map data, or the combination thereof for the at least one segment of the road to generate one or more aggregated values. The method further comprises generating output data corresponding to presence of the physical divider in the at least one segment of the road by a machine learning model based on the one or more aggregated values as an input to the machine learning model.

TECHNOLOGICAL FIELD

An example embodiment of the present invention relates to training amachine learning model, and more particularly, to using the trainedmachine learning model for predicting presence of one or more physicaldividers on at least one segment of a road.

BACKGROUND

Providing environmental awareness for vehicle safety, particularly inautonomous driving, has been a primary concern for automobilemanufacturers and related service providers. For example, knowingwhether physical dividers (e.g., structural separators) exist betweentravel lanes of a road segment can be an indicator that there is lesspotential for inter-lane accidents or collisions. Mapping these physicaldividers, however, has historically been resource-intensive. Recently,some machine learning models have been developed for predicting thepresence of a physical divider. Such machine learning models are trainedby passing sets of raw sensor data to a modelling system for which themachine learning model has to be called for each set, which can take along time and sometimes may even not be feasible. Accordingly, serviceproviders face significant technical challenges to more efficientlydetect and map physical dividers on road segments.

Some Example Embodiments

Therefore, there is a need to provide a more efficient approach fortraining a machine learning model for detection of one or more physicaldividers using vehicular sensor data and map data.

According to one embodiment, a computer-implemented method forprediction of a physical divider on a road is provided. The methodcomprises retrieving vehicular sensor data, map data, or a combinationthereof captured over a predefined period of time for at least onesegment of the road. The method further comprises aggregating therespective vehicular sensor data, map data, or the combination thereoffor the at least one segment of the road to generate one or moreaggregated values. The method further comprises generating output datacorresponding to presence of the physical divider in the at least onesegment of the road by a machine learning model based on the one or moreaggregated values as an input to the machine learning model.

According to another embodiment, a system for prediction of a physicaldivider on a road is provided. The system comprises at least one memoryconfigured to store instructions and at least one processor configuredto execute the instructions to retrieve vehicular sensor data, map data,or a combination thereof captured over a predefined period of time forat least one segment of the road. The system is also caused to aggregatethe respective vehicular sensor data, map data, or the combinationthereof for the at least one segment of the road to generate one or moreaggregated values. The system is further caused to generate output datacorresponding to presence of the physical divider in the at least onesegment of the road by a machine learning model based on the one or moreaggregated values as an input to the machine learning model.

According to yet another embodiment, a non-transitory computer-readablestorage medium having stored thereon computer-executable instructionswhich when executed by a computer, cause the computer to executeoperations for prediction of a physical divider on a road, theoperations comprising retrieving vehicular sensor data, map data, or acombination thereof captured over a predefined period of time for atleast one segment of the road. The operations also comprise aggregatingthe respective vehicular sensor data, map data, or the combinationthereof for the at least one segment of the road to generate one or moreaggregated values. The operations also comprise generating output datacorresponding to presence of the physical divider in the at least onesegment of the road by a machine learning model based on the one or moreaggregated values as an input to the machine learning model.

According to one or more embodiments, the vehicular sensor data isretrieved from a plurality of in-vehicle sensors installed in multiplevehicles traveling the road or from an original equipment manufacturer(OEM) cloud of the multiple vehicles. The vehicular sensor datacomprises continuous vehicular sensor data including physical divideraverage distance, physical divider maximum distance, physical dividerminimum distance, or a combination thereof. The physical divider flag isdetermined by processing the vehicular sensor data related to a numberof positive observations of the physical divider by unique vehiclestraveling the segment of the road. The vehicular sensor data furthercomprises categorical vehicular sensor data including a physical dividermeasurement event type, a physical divider flag, or a combinationthereof. The map data includes a functional class, a speed limit, apresence of a road sign, a bi-directionality, a number of lanes, a speedcategory, a distance to a point of interest, or a combination thereof.The retrieved vehicular sensor data is uniformed during aggregationthereof. The one or more aggregated values generated from the continuousvehicular sensor data includes at least one of a standard deviation anda mean of each of physical divider average distance, physical dividermaximum distance, physical divider minimum distance, or a combinationthereof. Further, the one or more aggregated values generated from thecategorical vehicular sensor data includes selected categorical values,using a voting algorithm, for each of the physical divider measurementevent type, the physical divider flag, or a combination thereof. The oneor more aggregated values are collected over an extended period of timefor use in training of the machine learning model for prediction ofpresence of the physical divider. Herein, the predefined period of timeis smaller than the extended period of time. The predefined period oftime may be approximately 1 day or any suitable period of time as may berequired for accurate prediction of the physical divider, and theextended period of time may be approximately 3 months or any othersuitable period of time that is longer than the predefined period oftime.

In addition, for various example embodiments of the invention, thefollowing is applicable: a method comprising facilitating a processingof and/or processing (1) data and/or (2) information and/or (3) at leastone signal, the (1) data and/or (2) information and/or (3) at least onesignal based, at least in part, on (or derived at least in part from)any one or any combination of methods (or processes) disclosed in thisapplication as relevant to any embodiment of the invention.

For various example embodiments of the invention, the following is alsoapplicable: a method comprising facilitating access to at least oneinterface configured to allow access to at least one service, the atleast one service configured to perform any one or any combination ofnetwork or service provider methods (or processes) disclosed in thisapplication.

For various example embodiments of the invention, the following is alsoapplicable: a method comprising facilitating creating and/orfacilitating modifying (1) at least one device user interface elementand/or (2) at least one device user interface functionality, the (1) atleast one device user interface element and/or (2) at least one deviceuser interface functionality based, at least in part, on data and/orinformation resulting from one or any combination of methods orprocesses disclosed in this application as relevant to any embodiment ofthe invention, and/or at least one signal resulting from one or anycombination of methods (or processes) disclosed in this application asrelevant to any embodiment of the invention.

For various example embodiments of the invention, the following is alsoapplicable: a method comprising creating and/or modifying (1) at leastone device user interface element and/or (2) at least one device userinterface functionality, the (1) at least one device user interfaceelement and/or (2) at least one device user interface functionalitybased at least in part on data and/or information resulting from one orany combination of methods (or processes) disclosed in this applicationas relevant to any embodiment of the invention, and/or at least onesignal resulting from one or any combination of methods (or processes)disclosed in this application as relevant to any embodiment of theinvention.

In various example embodiments, the methods (or processes) can beaccomplished on the service provider side or on the mobile device sideor in any shared way between service provider and mobile device withactions being performed on both sides.

For various example embodiments, the following is applicable: Anapparatus comprising means for performing a method of the claims.

Still other aspects, features, and advantages of the invention arereadily apparent from the following detailed description, simply byillustrating a number of particular embodiments and implementations,including the best mode contemplated for carrying out the invention. Theinvention is also capable of other and different embodiments, and itsseveral details can be modified in various obvious respects, all withoutdeparting from the spirit and scope of the invention. Accordingly, thedrawings and description are to be regarded as illustrative in nature,and not as restrictive.

BRIEF DESCRIPTION OF THE DRAWINGS

The embodiments of the invention are illustrated by way of example, andnot by way of limitation, in the figures of the accompanying drawings:

FIG. 1 is a diagram of a system capable of providing prediction ofphysical dividers, according to one embodiment;

FIG. 2 is a diagram illustrating examples of physical dividers,according to one embodiment;

FIG. 3 is a diagram illustrating a process for creating a physicaldivider overlay for segments of a road, according to one embodiment;

FIG. 4 is a diagram of the components of a physical divider platform,according to one embodiment;

FIG. 5A is a flowchart of a method for prediction of a physical divideron a road, according to one embodiment;

FIG. 5B is a flowchart of a process for controlling an autonomousvehicle using prediction of the physical divider on the road, accordingto one embodiment;

FIG. 5C is a schematic for exemplary daily updation of physical dividersdata in a map, according to one embodiment;

FIG. 5D is a schematic for exemplary quarterly re-training of machinelearning model, according to one embodiment;

FIG. 6 is a diagram illustrating an example of a vehicle equipped withsensors to support machine learning for use in prediction of physicaldividers, according to one embodiment;

FIG. 7A and FIG. 7B are depictions of a tabular representation ofaggregated data for an exemplary stretch of road, according to oneembodiment;

FIGS. 8A and 8B are diagrams of example architectures for providingmachine learning of physical dividers, according to one embodiment;

FIGS. 9A and 9B are diagrams of example user interfaces based onphysical dividers predicted by machine learning, according to onembodiment;

FIG. 10 is a diagram of a geographic database, according to oneembodiment;

FIG. 11 is a diagram of hardware that can be used to implement anembodiment;

FIG. 12 is a diagram of a chip set that can be used to implement anembodiment; and

FIG. 13 is a diagram of a mobile terminal (e.g., handset or vehicle orpart thereof) that can be used to implement an embodiment.

DESCRIPTION OF SOME EMBODIMENTS

Examples of a method, system, and computer program for training amachine learning model for detection of a physical divider are disclosedherein. In the following description, for the purposes of explanation,numerous specific details are set forth in order to provide a thoroughunderstanding of the embodiments of the invention. It is apparent,however, to one skilled in the art that the embodiments of the inventionmay be practiced without these specific details or with an equivalentarrangement. In other instances, well-known structures and devices areshown in block diagram form in order to avoid unnecessarily obscuringthe embodiments of the invention.

FIG. 1 is a diagram of a system 100 capable of providing machinelearning model for predicting physical dividers on a road, according toone embodiment. Having knowledge of whether physical dividers arepresent or absent on a road segment can provide important situationalawareness and improved safety to vehicles, particularly autonomousvehicles that operate with reduced or no human driver input. In otherwords, an understanding of where other vehicles and traffic objects maypotentially be and what they might do is important for an autonomousvehicle to safely plan a route. For example, as shown in FIG. 1, a road101 may support bi-directional traffic with a first vehicle 103traveling in one direction and a second vehicle 105 traveling in theopposite direction. In this example, a first physical divider 107 a ispresent between the vehicle travel lanes of the road 101, and a secondphysical divider 107 b is present between the one of the vehicle travellanes and a pedestrian lane 109 adjacent to the road 101 (alsocollectively referred to as physical dividers 107). The presence of thephysical dividers 107 can improve safety by reducing the probability ofoncoming traffic collisions, or collisions with pedestrians or othernon-vehicular traffic.

In one embodiment, a physical divider 107 is a structural separator thatis a fixed roadside or median entity that prevents vehicles and/orpedestrians traveling in one lane from crossing into or accessing otherlanes of the road (and/or from crossing between different traffic flowdirections). The physical divider 107 can be, for instance, a physicalbarrier, or provide enough clearance between different lanes or trafficflow directions so that potential cross-over traffic is minimized orprevented. FIG. 2 is a diagram illustrating examples of physicaldividers. As shown, a physical divider can include, but is not limitedto, (1) a solid wall 201 (e.g., a concrete barrier), (2) a median 203that is sufficiently wide to separate reduce potential crossover trafficto a threshold probability, (3) a row of columns 205, (4) a row of trees207, etc. It is noted that, in one embodiment, a physical divider 107can be made of any type material or construction provided that itreduces, minimizes, or prevents potential crossover between travel lanesor traffic flow directions.

Because of the diversity of physical dividers 107 and the variability oftheir installation along roadways, mapping the presence or absence ofphysical dividers 107 historically has been a resource-intensive effort,typically employing data collection vehicles to travel the roads forhuman observers to manually annotate physical dividers in a map data(e.g., a geographic database 111). As a result, map providers havetraditionally been only able to map a small percentage of the physicaldividers 107. Moreover, such mapping is of static nature and may notreflect any dynamic change in the positioning or presence of thephysical dividers. Some machine learning models have been developed forpredicting the presence of a physical divider. However, such machinelearning models are trained by passing sets of raw sensor data to amodelling system for which the machine learning model have to be calledfor each set, which can take a long time and sometime may even not befeasible. Therefore, providing a more efficient approach for training amachine learning model for detection of the physical dividers 107 usingvehicular sensor data (sometimes, simply referred to as “sensor data”)and map data presents a significant technical problem.

To address this problem, a system 100 of the present disclosure (asillustrated in FIG. 1) introduces a capability to use machine learningmodel (e.g., a supervised learning algorithm) to process vehicularsensor data and/or map data to determine the probability of a physicaldivider existing on a segment of road. In one embodiment, a physicaldivider platform 113 can train a machine learning model 115 (e.g.,Random Forest, Decision Tree, Neural Net, or equivalent) to predictphysical dividers 107 on a road segment of interest, based on map dataassociated with the segment of interest, vehicular sensor data collectedfrom vehicles (e.g., the vehicle 103 equipped with an array of sensors117), an OEM cloud for the vehicles, or a combination thereof. In oneembodiment, at least one vehicle (e.g., vehicle 103) can include aphysical divider module 119 for performing one or more functionsassociated with machine learning of physical dividers alone or incombination with the physical divider platform 113. In one or moreembodiments, the components and processes of the system 100 of thepresent disclosure can also be implemented in a stand-alone apparatus(similar to the system 100) without any limitations.

In one embodiment, the physical divider platform 113 and/or the physicaldivider module 119 can then use the trained machine learning model 115to calculate the probability of a physical divider 107 (i.e., astructural separator) being on the road segment of interest based on themap data and/or vehicle sensor information associated with the segmentof interest. If the calculated probability is above a threshold value,then the physical divider platform 113 can output data indicating apredicted presence of the physical divider 107 on the segment. If thecalculated probability is below the threshold value, then the physicaldivider platform 113 can output data indicating a predicted absence ofthe physical divider 107 on the segment. Thus, example embodimentsillustrated herein, provide for a robust system that minimizes thenumber of times the machine learning model 115 is called for predictingpresence of the physical divider 107.

In one embodiment, the goal of detecting or predicting a physicaldivider 107 is to determine the probability of other relatedcharacteristics such as, but not limited to: (1) probability of oncomingor opposite traffic on the segment of interest (OPPO) (e.g., if there isno physical divider 107 between opposite traffic flows on the segment,the probability of oncoming traffic or a collision with oncoming trafficcan be higher); and (2) probability of the presence of vulnerable roadusers (VRU) (e.g., if there is no physical divider 107 between vehicularand non-vehicular traffic, then there is a greater possibility of apossible collection with VRU's, e.g., pedestrians, bicyclists, etc.). Inone embodiment, the physical divider platform 113 can predict the othercharacteristics that are related to or associated with thepresence/absence of a physical divider 107 directly with the machinelearning model 115 (e.g., if the machine learning model 115 has beentrained accordingly).

In addition or alternatively, the physical divider platform 113 can usea rule-based approach applied on the predicted presence/absence of aphysical divider 107 to predict the other characteristics. For example,if there is a no physical divider 107 predicted on the segment and thesegment supports bi-directional traffic (e.g., as determined from mapdata stored in the geographic database 111), then the physical dividerplatform 113 can also predict that OPPO and VRU probabilities are high.It is noted that this rule is provided by way of illustration and not asa limitation. It is contemplated that the physical divider platform 113can use any equivalent process or means to determine OPPO, VRU, and/orany other related characteristics from a predicted presence/absence of aphysical divider 107 on a segment of interest.

In one embodiment, the physical divider platform 113 segments each ofroad represented in a map database (e.g., the geographic database 111)into segments of a predetermined length (e.g., 5-meter segments;however, any other segment size may be supported by embodiments of theinvention described herein). Then, the physical divider platform 113 canmake physical divider predictions for each segment of the road. FIG. 3is a diagram illustrating an example process for creating a physicaldivider overlay 301 for segments of a road 303, according to oneembodiment. In one embodiment, the physical divider overlay 301 is adata structure that can be associated with the geographic database 111.The physical divider overlay 301 stores, for instance, parametricrepresentations of predicted physical dividers 107, OPPO, VRU, and/orother related attributes in association with corresponding segments ofthe road 303.

As shown, the physical divider platform 113 segments the road 303 intosegments 305 a-305 f (also collectively referred to as segments 305).The length of each segment provides for a corresponding level ofgranularity of the detected physical dividers. For example, a defaultlength of the segments 305 can be 5-meters so that each segment 305represents a 5-meter long portion of the road 303. In one embodiment,shorter segment lengths can be used if a higher resolution of detectionof the physical divider 307 a-307 b is desired, and longer segmentlengths can be used to reduce memory storage requirements. It may beappreciated that the last segment may be less than the initiallypredefined fixed length. The physical divider platform 113 can thencollect map data and vehicular sensor data from vehicles (e.g., vehicles103 and 105) as the vehicles traverse each segment 305 a-305 f of theroad 303.

FIG. 4 is a diagram of the components of a physical divider platform 113and/or physical divider module 119, according to one embodiment. By wayof example, the physical divider platform 113 and/or physical dividermodule 119 includes one or more components for providing machinelearning of physical dividers according to the various embodimentsdescribed herein. It is contemplated that the functions of thesecomponents may be combined or performed by other components ofequivalent functionality. In this embodiment, the physical dividerplatform 113 and/or physical divider module 119 include a sensor dataingestion module 401, a map data module 403, a data processing module405, a machine learning module 407, a data publication module 409, and avehicle control module 411. The above presented modules and componentsof the physical divider platform 113 and/or physical divider module 119can be implemented in hardware, firmware, software, or a combinationthereof. Though depicted as separate entities in FIG. 1, it iscontemplated that the physical divider platform 113 and/or physicaldivider module 119 may be implemented as a module of any of thecomponents of the system 100; e.g., a component of the vehicle 103,services platform 121, services 123 a-123 n (also collectively referredto as services 123), etc. In another embodiment, one or more of themodules 401-411 may be implemented as a cloud based service, localservice, native application, or a combination thereof. The functions ofthe physical divider platform 113, physical divider module 119, andmodules 401-411 are discussed with respect to FIGS. 5A-9B below.

FIG. 5A is a flowchart of a method 500 for prediction of a physicaldivider on a road using a machine learning model (e.g., machine learningmodel 115), according to one embodiment. In various embodiments, thephysical divider platform 113, physical divider module 119, and/or anyof the modules 401-411 may perform one or more portions of the method500 and may be implemented in, for instance, a chip set including aprocessor and a memory as shown in FIG. 12. As such, physical dividerplatform 113, physical divider module 119, and/or any of the modules401-411 can provide means for accomplishing various parts of the method500, as well as means for accomplishing embodiments of other processesdescribed herein in conjunction with other components of the system 100.Although the method 500 is illustrated and described as a sequence ofsteps, its contemplated that various embodiments of the method 500 maybe performed in any order or a combination and need not include all ofthe illustrated steps. The method 500, for instance, describes theprocess for collecting map and/or vehicular sensor data to train themachine learning model 115 to predict physical dividers for a given roadsegment.

In step 501, the method 500 includes retrieving vehicular sensor data,map data, or a combination thereof captured over a period of time for atleast one segment of a road. Herein, the physical divider platform 113can use any combination of the captured map and/or vehicular sensor datato create a training data set for training the machine learning model115. In one embodiment, the composition of the training data set can bebased on a target level of prediction accuracy. For example, retrievingboth map data and sensor data can potentially provide for increasedpredictive accuracy over either type of data individually. However, whenthe target predictive accuracy can be achieved by using map data orsensor data alone, the physical divider platform 113 can reduce theresource-burden associated with having to collect both datasets. In anexemplary embodiment, the predefined period of time is approximately oneday; however, it may be contemplated that the predefined period of timemay be suitably adjusted depending on the requirements of the machinelearning model 115 for the training data.

In one embodiment, the sensor data ingestion module 401 can be used toretrieve vehicular sensor data, and the map data module 403 can be usedto retrieve map data for given segment of a road. As noted above, thephysical divider platform 113 can segment a road into discrete segmentsof a predetermined length (e.g., 5-meters) to facilitate processing. Inthis case, for each segment of road, the sensor data ingestion module401 extracts raw data collected from vehicle sensors (e.g., camera,radar, LiDAR, etc.) of vehicles traveling on the segment of interest. Insome example embodiments, the raw data may be reterived directly fromthe original equipment manufacturer (OEM) sensors or from an OEM cloud.For example, the raw sensor data can include, but is not limited, to across-sensor consistency distribution (e.g., minimum, maximum, mean,standard deviation, or other statistical parameter of the distributioncan be used), a physical divider distance distribution (e.g., alsominimum, maximum, mean, standard deviation, or other statisticalparameter of the distribution can be used), a height of the physicaldivider 107, a vehicle speed, a physical divider type (e.g., seeexamples of FIG. 3), a physical divider sample point count, or acombination thereof. It is contemplated that any sensed parameter of thevehicle, the physical divider 107, or the road segment (e.g., weather,time of day, visibility, etc.) can be collected as raw sensor data forprocessing by the sensor data ingestion module 401.

FIG. 6 is a diagram illustrating an example of a vehicle 601 equippedwith sensors to capture the vehicular sensor data support in order totrain the machine learning model for prediction of physical dividers,according to one embodiment. As shown, a vehicle 601 is equipped with acamera sensor 603, a LiDAR sensor 605, and a radar sensor 607. Each ofthese sensors 603-607 are capable of sensing the presence of a physicaldivider 107 individually. However, each sensor 603-607 has a respectivedrawback. For example, LiDAR data or camera data of the physical divider107 can be obscured if there an obstruction (e.g., another vehicle)between the vehicle 601 and the physical divider 107. Similarly, radarsignals may pass through the physical divider 107 when it is made orporous material or other material that reflects radar signals poorly,have a low height, etc. These weaknesses can potentially lead to lessreliable or less accurate detection of physical dividers 107.

To address the individual technical weaknesses of the sensors 603-607,the sensor data ingestion module 401, for instance, can performin-vehicle sensor fusion of the physical divider 107 or structuralseparator. In other words, the sensor data ingestion module 401 can usemultiple different sensors 603-607 to determine a consistency ofdetection of the physical divider 107 among the different sensors603-607. For example, with respect to cross-sensor consistency, thesensor data may be collected from at least two sensors of the vehicle.Then the various sensed characteristics of the detected physical divider107 can be compared (e.g., location from the vehicle 601, detectedheight of the physical divider 107, etc.) for consistency (e.g., bycalculating a percent difference or equivalent). In addition, becauseeach of the sensors are capable of sampling multiple times per second orfaster, a distribution of the cross-sensor consistency can be determinedfrom the sampling set. In one embodiment, the cross-sensor consistencydistribution can be one parameter retrieved by the sensor data ingestionmodule 401 as feature indicating a consistency of detecting the physicaldivider 107 between each of at least two of the sensors 603-607.

In one embodiment, the sensor data ingestion module 401 can alsopre-process the collected raw sensor data to provide the vehicularsensor data for training of the machine learning model 115 (as discussedlater). In the present embodiments, the vehicular sensor data includescontinuous vehicular sensor data including physical divider averagedistance, physical divider maximum distance, physical divider minimumdistance, or a combination thereof. Further, the vehicular sensor datacomprises categorical vehicular sensor data including a physical dividermeasurement event type, a physical divider flag, or a combinationthereof.

In one embodiment, the vehicular sensor data is retrieved from aplurality of in-vehicle sensors installed in multiple vehicles travelingthe road. The sensor data ingestion module 401 can use such sensor datafrom multiple vehicles traveling on the same road segment to determineadditional attributes or features for machine learning. For example, thesensor data ingestion module 401 can process the sensor data from aplurality of vehicles traveling the segment of the road to determine orcalculate a derivative feature. A derivative feature refers to anyfeature or attribute that can be calculated or processed from the rawdata from multiple vehicles (e.g., not directly sensed by a sensor603-607). For example, the derivative feature can include, but is notlimited to, the number of positive observations of the physical divider107 by unique vehicles traveling the road segment. In one embodiment,this number of positive observations can be normalized by the totalnumber of drives or vehicles that passed the given segment. Thereby, thephysical divider flag can be determined by processing the vehicularsensor data related to a number of positive observations of the physicaldivider 107 by unique vehicles traveling the segment of the road.

In an embodiment where the map data is used alone or in combination withthe sensor data, the map data module 403 can retrieve requested map datafor a road segment of interest by performing a location-based query ofthe geographic database 111 or equivalent. By way of example, the mapdata can include any attribute of the road segment or corresponding maplocation stored in the geographic database 111. The retrieved map datacan include, but is not limited to, a functional class, a speed limit, apresence of a road sign (e.g., school zone sign), a bi-directionality ofthe road, a number of lanes, a speed category, a distance to a nearbypoint of interest, or a combination thereof. The map data can alsoinclude the presence of non-vehicular travel lanes on the road segment(e.g., sidewalks, bicycle paths, etc.).

In one embodiment, the sensor data ingestion module 401 can retrievesensor data directly from vehicles with connected communicationscapabilities (e.g., cellular or other wireless communications equippedvehicles) or from an Original Equipment Manufacturer (OEM) provider(e.g., automobile manufacturer) operating a OEM platform (e.g., aservices platform 123) that collects sensor data from vehiclesmanufactured by or otherwise associated with the OEM. The retrieval ofthe sensor data and/or the map data can occur in real-time or nearreal-time, continuously, periodically, according to a schedule, ondemand, etc.

In one embodiment, the sensor data can be provided as location tracedata in which each sensor data sampling point is associated withlocation coordinates of the collection vehicle. The location coordinatescan be determined from location sensors (e.g., GPS/satellite-basedlocation sensors or equivalent) and recorded with the sensor data. Inthis case, the sensor data ingestion module 401 can perform a mapmatching (e.g., using any map matching technique known in the art) ofthe location data of each sensor data sampling point to identify whichroad segment the sensor data sampling point belongs. In other words,each location trace is associated with segments of map road links andtransformed into sensor data observations for a particular segment ofthe road link. For example, the data ingestion module 401 can use apath-based map matching algorithm by calculating the collectionvehicle's direction of travel from the time stamp and GPS points presentin the retrieved sensor data.

In step 503, the method 500 includes aggregating the respectivevehicular sensor data, map data, or a combination thereof for the atleast one segment of the road to generate one or more aggregated values.That is, after retrieval of the map data, vehicular sensor data, and/orderivative feature or a combination thereof for a given segment of theroad for the predefined period of time is aggregated. In the presentembodiments, the data is aggregated in the data processing module 405.As discussed, the predefined period of time is approximately 1 day.Thus, the system 100 can have daily update of the map for physicaldividers by the machine learning model 115 (as illustrated in FIG. 5C).

For generating one or more aggregated values, the method 500 includesdetermining at least one of a standard deviation and a mean of each ofphysical divider average distance, physical divider maximum distance,physical divider minimum distance, or a combination thereof; andgenerating the one or more aggregated values based on the determined atleast one of the standard deviation and the mean of each of physicaldivider average distance, physical divider maximum distance, physicaldivider minimum distance, or a combination thereof. Additionally, themethod 500 includes selecting categorical values, using a votingalgorithm, for each of the physical divider measurement event type, thephysical divider flag, or a combination thereof; and generating the oneor more aggregated values based on the selected categorical values foreach of the physical divider measurement event type, the physicaldivider flag, or a combination thereof.

In one embodiment, the method 500 includes uniforming the vehicularsensor data retrieved from the plurality of in-vehicle sensors (such as,the sensors 603-607) while aggregation thereof. The vehicular sensordata retrieved from the plurality of in-vehicle sensors is uniformed inthe data processing module 405. For instance, in one example, thephysical divider average distance (which is measured in meters) isrounded to 2 decimal places. Similarly, the physical divider maximumdistance and the physical divider minimum distance (which are alsomeasured in meters) are rounded to 2 decimal places. It may beunderstood that this is being done so as to enable using of vehicularsensor data from different OEM vehicles that may have driven by thesegment of the road, collecting the data.

In one or more embodiments, the one or more aggregated values arecollected over an extended period of time for use in training of themachine learning model 115. In the present embodiments, the predefinedperiod of time may be smaller than the extended period of time. In anexemplary embodiment, the extended period of time is approximately 3months. This may be done, since the system 100 is configured to onlyconsider the last 3 months of data, and any old data that is no longerrepresentative of the road network or recent vehicle observations aredropped. Therefore, the machine learning model 115 may be re-trainedquarterly (i.e., after every 3 months) so that the most importantfeatures for the machine learning model, i.e., the map features aregenerally always updated (as illustrated in FIG. 5D).

FIG. 7A and FIG. 7B depict tabular representations of aggregated datafor a stretch of road with three (3) number of segments (i.e., segment1, segment 2, and segment 3). FIG. 7A depicts the sensor data aggregatedfor the stretch of the road with three segments. FIG. 7B depicts the mapdata aggregated for the stretch of the road with three segments.Further, the given representational data is shown to be collected fromthree (3) number of vehicles (i.e. vehicle 1, vehicle 2 and vehicle 3)which have traversed at least the target segment of the road previouslyin the predefined period of time (i.e., in the last day). A typicalapproach would have been to pass each vehicle's sensor informationindependently to the machine learning model. In such case, effectivelyfor segment 1, the machine learning model would need to be called three(3) number of times in one day for that single segment which is verytime consuming. The three (3) number of calls to the machine learningmodel, with each time passing the feature from a single vehicle alongwith map data, is made as follows. In first call to machine learning forprediction, all the features from vehicle 1 (i.e. d1, me1, ma1, . . . )and the map feature (i.e. FC1, 3,100) are passed to the machine learningmodel and the model will predict PD ON or PD OFF for segment 1.Similarly, second call to machine learning for prediction, all thefeatures from vehicle 2 (i.e. d2, me2, ma2, . . . ) and the map feature(i.e. FC2, 4, 80) are passed to the machine learning model and the modelwill predict PD ON or PD OFF for segment 2. Yet again, third call tomachine learning for prediction, all the features from vehicle 3 (i.e.d3, me3, ma3, . . . ) and the map feature (i.e. FC3, 3, 60) are passedto the machine learning model and the model will predict PD ON or PD OFFfor segment 3. Generally, in typical approach, if there are ‘n’ vehicleobservations within a day then ‘n’ number of calls are required to themachine learning model and the machine learning model will make ‘n’predictions for the same segment (e.g. segment 1 in this case); althoughfor the ‘n’ number of calls to the machine learning model, the same mapdata is employed. For example, with some segments having thousands ofobservations in one day, the typical training of machine learning modelmay require calls to be made thousands of time for a single segment,which is not feasible.

In the present embodiments, the aggregated values, from the vehicularsensor data, is generated as discussed hereinafter. For continuous data,such as, distance in meters, median, maximum, minimum, standarddeviation, and consistency, etc., the data processing module 405 wouldtake the mean across all three vehicles. For example, for distance, theaggregated feature would be (d1+d2+d3)/3; and similarly, the otheraggregated features would be (me1+me2+me3)/3, (ma1+ma2+ma3)/3,(mi1+mi2+m13)/3, (sd1+sd2+sd3)/3 and (c1+c2+c3)/3. For categoricalsensor data, such as PD flag and event type, the voting algorithms isconsidered for aggregating the vehicular sensor data and the categorywith the highest vote wins. Herein, the PD flag “ON” represents positiveobservation for the physical divider on the given segment of the road,and the PD flag “OFF” represents negative observation for the physicaldivider on the given segment of the road. In the present example, for PDflag, there are two observations for PD “ON” and only one observationfor PD “OFF” (as listed in FIG. 7A); thus for segment 1, the aggregatedPD flag feature would be PD “ON”. Further, the event type could be oneof “INITIAL,” “UPDATE” and “CANCEL.” Herein, the event type “INITIAL”represents reading(s) for the given segment of the road for generatingthe map therefor, the event type “UPDATE” represents reading for thegiven segment of the road for updating the corresponding map, and theevent type “CANCEL” represents reading for the given segment of the roadfor cancelling the existing map. In the present examples, there are twoobservations for “UPDATE” and only one observation for “CANCEL” (aslisted in FIG. 7A); thus for segment 1, the aggregated event typefeature would be “UPDATE”. Furthermore, the aggregated values for mapdata (as listed in FIG. 7B) would be FC1, number of lanes would be equalto ‘3’ and the speed limit would be equal to ‘100’.

It may be appreciated that the method 500 of the present disclosureusing the aggregated values would be required to make only limitednumber of calls, such as only one call, to the machine learning model115, and the machine learning model 115 will then make a singleprediction for ‘segment 1’ (as discussed in the subsequent paragraphs).It may be appreciated that although the process has been described onlyfor aggregation and extraction of data for ‘segment 1’, similarprocesses can also be applied for aggregation and extraction of data for‘segment 2’ and ‘segment 3’ without any limitations.

In step 505, the method 500 includes generating output datacorresponding to presence of the physical divider. This may includepredicting presence of the physical divider in the at least one segmentof the road by a machine learning model (such as, the machine learningmodel 115) based on the one or more aggregated values as an input to themachine learning model. For this purpose, the one or more aggregatedvalues are passed to the machine learning module 407. The machinelearning module 407 may process the one or more aggregated values togenerate a feature vector comprising the attributes indicated in thevehicular sensor data, the map data, or a combination thereof. Thisfeature vector may then be provided as an input to the machine learningmodel 115. When used for training the machine learning model 115, thefeature vector may be part of a training data set. When used for actualprediction, the feature vector is provided as an input to a trainedmachine learning model 115 for predicting the presence/absence of aphysical divider 107 at a corresponding segment of interest.

The present approach of aggregating the features and passed once to themachine learning model provides fast map updates for each segment in themap. In one embodiment, the rankings for various features (as describedabove) according to their predictive index is given below in Table 1.

TABLE 1 Feature No Feature name Feature type 2 Display bits Map features7 Functional class Map features 12 Speed category Map features 6 Fromreference number lane Map features 3 To reference speed limit Mapfeatures 4 From reference speed limit Map features 5 To reference numberlane Map features 1 Access bits Map features 8 Long haul Map features 13Divider Map features 11 Lane category Map features 10 Intersectioncategory Map features 15 From reference physical num lane Map features14 To reference physical num lane Map features 9 Stub link Map features0 Special attributes Map features 19 median Sensor feature 16 Distancein meters Sensor feature 17 Consistency Sensor feature 21 Standarddeviation Sensor feature 18 Flag Sensor feature 20 Recognition Sensorfeature

In some examples, with respect to the training use case, after creatingthe feature vector as described above for inclusion in a training dataset, the machine learning module 407 retrieves ground truth data about aphysical divider 107 for the segment of the road. The ground truth data,for instance, indicates a true presence or a true absence of thephysical divider 107 on the segment of the road of interest. The machinelearning module 407 processes the sensor data, the map data, or acombination thereof in view of the ground truth data to train themachine learning model 115 to predict the physical divider using the mapdata, the sensor data, or a combination thereof as an input. Aspreviously discussed, the machine learning model 115 can be based on anysupervised learning model (e.g., Random Forest, Decision Tree, NeuralNet, Deep Learning, logistic regression, etc.). For example, in the caseof a neural network, the machine learning model 115 can consist ofmultiple layers or collections of one or more neurons (e.g., processingunits of the neural network) corresponding to a feature or attribute ofthe input data (e.g., the feature vector generated from the map and/orvehicular sensor data as described above).

This ground truth data can be collected using traditional or equivalenttechniques (e.g., manual human annotation of a collected sensor dataobservation to indicate presence or absence of a physical divider 107and/or its type or characteristics). For example, a map service providercan operate a fleet of map data collection vehicles that can moresophisticated, accurate, or different types of sensors (e.g., radar,cameras, LiDAR, etc.) than would normally be available in customervehicles. As described above, the physical divider is a fixed roadsideor a median structure that separates different traffic flow directionsor types (e.g., vehicular traffic vs. non-vehicular traffic). In oneembodiment, only segments for which ground truth data is collected orotherwise available are selected for training the machine learningmodel. In one example, when independent ground truth data is notavailable or otherwise not used, the machine learning module 407 can usethe underlying sensor data individually to estimate whether a physicaldivider is present or absent on the corresponding road segment. Forexample, image recognition can be performed on camera sensor datacollected from a vehicle traveling the road segment. If imagerecognition results in detecting the presence or absence a physicaldivider in the image data, the results can be used as pseudo-groundtruth data to train the machine learning classifier. In this way, whenoperating with independent ground truth data, map and sensor datacollected from one road segment can be used to train the machinelearning model 115 to predict the presence or absence of physicaldividers on other road segments.

During training, the machine learning module 407 uses a learner modulethat feeds feature vectors from the training data set (e.g., createdfrom map and/or sensor data as described above) into the machinelearning model 115 to compute a predicted matching feature (e.g.,physical divider and/or other related characteristic to be predicted)using an initial set of model parameters. For example, the targetprediction for the machine learning model 115 can be whether there is aphysical divider present for a given segment (e.g., 5-meter segment) ofa road of interest. In one embodiment, the machine learning model 115can also be used to model or predict shape, distance from vehicle to thephysical divider (e.g., from a vehicle reference point such as the rearaxle), and/or other attributes of the physical divider 107 if the groundtruth data contains attributes or feature labels.

In addition or alternatively, the machine learning model 115 can be usedto predict a road characteristic related to the physical divider. Forexample, the road characteristic related to the physical divider mayinclude, but is not limited to, a probability of oncoming traffic(OPPO), a presence of vulnerable road users (VRU), or a combinationthereof. In one embodiment, the prediction can be a binary prediction(e.g., physical divider/OPPO/VRU present or physical divider/OPPO/VRUabsent). In another embodiment, prediction can be a probability of thephysical divider/OPPO/VRU being present or absent (e.g., spanning anumerical range from 0 to 1, with 0 being no probability and 1 being thehighest probability).

In one embodiment, based on the collected map and/or sensor data, thephysical divider platform 113 predicts the presence or absence of thephysical dividers 307 a-307 b using a trained machine learning model(e.g., machine learning model 115) for each segment 305 to store in thephysical divider overlay 301. Table 2 below illustrates an example ofthe predictions based on the example of FIG. 3:

TABLE 2 Segment Physical Divider 307a Physical Divider 307b 305a PresentPresent 305b Present Present 305c Present Present 305d Present Present305e Absent Present 305f Absent Present

In some embodiments, the machine learning module 407 uses the trainedmachine learning model 115 to a generate a physical divider overlay of amap representation of a road network. For example, the machine learningmodule 407 can interact with the sensor data ingestion module 401 andmap data module 403 receive sensor data observations from OEM providersand/or vehicles traveling in the road network. The observations can thenbe used an input into the trained machine learning model 115 asdiscussed in more detailed below with respect to FIG. 5B. In oneembodiment, the physical divider overlay indicates a presence or anabsence of one or more physical dividers in the road network of the maprepresentation. In one embodiment, the physical divider overlay can alsoinclude other data related to a presence or an absence of one or morephysical dividers in the road network of the map representation such asa probability of oncoming traffic (OPPO), a presence of vulnerable roadusers (VRU), or a combination thereof as previously discussed.

In other words, in one embodiment, given the training data above, thephysical divider platform 113 can run a batch process (e.g., every 1 dayor any other predefined period of time) and extract the feature vectorsas described above, and pass the feature vectors to the already trainedmachine learning model 115. The trained machine learning model 115 willoutput whether the road segment (e.g., 5-meter segment) corresponding tothe input feature vector contains a physical divider or not. In oneembodiment, the data publication module 409 can then publish thephysical divider overlay in the geographic database 111 or equivalentfor access by end users (e.g., OEMs, vehicles, etc.).

In one embodiment, the predicted physical divider 107 can then be usedto determine how to operate an autonomous vehicle. For example, if aphysical divider 107 is predicted to be present based on map data and/orvehicle sensor data on a road segment (e.g., because no previouslydetermined ground truth data on physical dividers is available for theroad segment), then a more autonomous operation of the vehicle can bedisabled, and the driver is expected to drive in more of a manual mode(e.g., requiring the driver to hold the steering wheel as the vehicleoperates otherwise in autonomous mode, or to disable some or allautonomous operations). In one embodiment, other use cases includeupdating the physical divider overlay 301 and/or geographic database 111with the newly detected physical dividers. It is noted that these usescases are provided by way of illustration and not as limitations.Accordingly, it is contemplated that the predicted physical dividerinformation and/or related predicted attributes (e.g., OPPO, VRU, etc.),can be used for any other use case, application, and/or service.

FIG. 5B is a flowchart of a process 520 for controlling an autonomousvehicle using prediction of presence or absence of physical dividers bya trained machine learning model, such as the machine learning model115, according to one embodiment. In various embodiments, the physicaldivider platform 113, physical divider module 119, and/or any of themodules 401-411 may perform one or more portions of the process 520 andmay be implemented in, for instance, a chip set including a processorand a memory as shown in FIG. 12. As such, physical divider platform113, physical divider module 119, and/or any of the modules 401-411 canprovide means for accomplishing various parts of the process 520, aswell as means for accomplishing embodiments of other processes describedherein in conjunction with other components of the system 100. Althoughthe process 520 is illustrated and described as a sequence of steps, itscontemplated that various embodiments of the process 520 may beperformed in any order or a combination and need not include all of theillustrated steps.

In step 521, the sensor data ingestion module 401 and/or the map datamodule 403 collect map data, sensor data, or a combination thereof froma vehicle traveling on a road segment. This step is equivalent to step501 of the method 500 described above. However, in this use, the roadsegment of interest is a road segment for which a prediction of aphysical divider 107 or other related characteristic is requested.

In step 523, the machine learning module 407 processes the map data, thesensor data, or a combination thereof using a machine learning model,such as the machine learning model 115, to predict a presence or anabsence of a potential physical divider on the target road segment. Inone embodiment, the map data, sensor data, and/or any combinationdetermined therefrom are used to generate a feature vector of theattributes of the collected data as an input into the trained machinelearning model 115. In one embodiment, the machine learning model istrained using training map data, training sensor data, or a combinationthereof and ground truth data regarding a true presence or a trueabsence of a reference physical divider as described above with respectto the method 500.

In step 525, the vehicle control module 411 activates or deactivates anautonomous driving mode of the vehicle based on the predicted presenceor the predicted absence of the physical divider. In addition, oralternatively, the vehicle control module 411 can present a notificationto the driver or occupant of the vehicle prior to activating ordeactivating the autonomous mode. For example, the notification canalert the driver that a change in the autonomous mode will occur shortly(e.g., within a specified period of time). In another example, thenotification can provide the driver an option to accept or reject thepending change in autonomous driving mode, or select other alternatives(e.g., reroute the vehicle to road segments with physical dividers,etc.). In one embodiment, the autonomous driving mode is deactivatedbased on the predicted presence of the physical divider, and activatedbased on the predicted absence of the physical divider. By way ofexample, the vehicle can be an autonomous vehicle or highly assisteddriving vehicle that is capable of sensing its environment andnavigating within a road network without driver or occupant input. It isnoted that autonomous vehicles and highly assisted driving vehicles arepart of a spectrum of vehicle classifications that can span from noautomation to fully autonomous operation. For example, the U.S. NationalHighway Traffic Safety Administration (“NHTSA”) in its “PreliminaryStatement of Policy Concerning Automated Vehicles,” published 2013,defines five levels of vehicle automation:

-   -   Level 0 (No-Automation)—“The driver is in complete and sole        control of the primary vehicle controls—brake, steering,        throttle, and motive power—at all times.”;    -   Level 1 (Function-specific Automation)—“Automation at this level        involves one or more specific control functions. Examples        include electronic stability control or pre-charged brakes,        where the vehicle automatically assists with braking to enable        the driver to regain control of the vehicle or stop faster than        possible by acting alone.”;    -   Level 2 (Combined Function Automation)—“This level involves        automation of at least two primary control functions designed to        work in unison to relieve the driver of control of those        functions. An example of combined functions enabling a Level 2        system is adaptive cruise control in combination with lane        centering.”;    -   Level 3 (Limited Self-Driving Automation)—“Vehicles at this        level of automation enable the driver to cede full control of        all safety-critical functions under certain traffic or        environmental conditions and in those conditions to rely heavily        on the vehicle to monitor for changes in those conditions        requiring transition back to driver control. The driver is        expected to be available for occasional control, but with        sufficiently comfortable transition time.”; and    -   Level 4 (Full Self-Driving Automation)—“The vehicle is designed        to perform all safety-critical driving functions and monitor        roadway conditions for an entire trip. Such a design anticipates        that the driver will provide destination or navigation input,        but is not expected to be available for control at any time        during the trip. This includes both occupied and unoccupied        vehicles.”

In one embodiment, the various embodiments described herein areapplicable to vehicles that are classified in any of the levels ofautomation (levels 0-4) discussed above. For example, in the case ofautonomous modes of operation, the vehicle can automatically react todetected physical dividers, OPPO, VRU, etc. (e.g., by automaticallyrerouting, slowing down, etc.). Even in the case of completely manualdriving (e.g., level 0), the vehicle can present an alert ornotification of any detected physical dividers, OPPO, VRU, etc. toprovide greater situational awareness and improve safety for drivers.

In another use case of a physical divider prediction, in addition to orinstead of autonomous vehicle control, the data publication module 409can initiate an update of physical divider overlay of a map databasebased on the predicted presence or the predicted absence of the physicaldivider, OPPO, VRU, etc. on the road segment (step 527). For example, ifthe segment has been previously unmapped, the predicted physicaldivider/OPPO/VRU can be transmitted for possible inclusion in physicaldivider overlay of the geographic database 111. The physical dividerplatform 113 can use any criteria for determining whether a new physicaldivider prediction should be incorporated into an existing physicaldivider overlay. For example, if the report is from a trusted vehicle(e.g., a mapping vehicle operated by a map provider), a singleprediction can be used to update the overlay. If the report is from auser vehicle, the physical divider platform 113 may update the overlayonly if the report meets predetermined criteria (e.g., confirmed by apredetermined number of other user vehicles, has calculated probabilityabove a threshold value, etc.).

FIGS. 8A and 8B are diagrams of example architectures for providingmachine learning of physical dividers, according to one embodiment. FIG.8A illustrates an example architecture 801 in which the machine learningmodel 115 is instantiated on a network component (e.g., the physicaldivider platform 113). In this way, the processing needed by the machinelearning model 115 is provided on the server side, where computingresources (e.g., processing power, memory, storage, etc.) is generallygreater than at a local component (e.g., the vehicle 103).

Under the architecture 801, an OEM platform 803 (e.g., operated byautomobile manufacturer) collects sensor data observations from vehiclesas they travel in a road network. The OEM platform 803 sends theobservations to the physical divider platform 113 (e.g., typicallyoperated by a map or other service provider) for ingestion and archival.The physical divider platform 113 (e.g., where the trained machinelearning model 115 is instantiated) trains the machine learning model115 and thereafter processes the received observations using the machinelearning model 115 to predict physical dividers, OPPO, VRU, etc. Thesephysical divider/OPPO/VRU predictions are then fused map attributeinformation to produce the physical divider overlay 805. The physicaldivider platform 113 can then publish the physical divider overlay 805for delivery to the vehicle 103 either directly or through the OEMplatform 803.

FIG. 8B illustrates an alternative architecture 821 in which no physicaldivider overlay is delivered to the vehicle 103. Instead, a trainedmachine learning model 115 is instantiated at a local component orsystem of a vehicle 103 traveling the road network. In this way, thelocal component uses the machine learning model 115 to provide a localprediction of the physical divider (e.g., physical divider predictions823) based on locally collected map and/or sensor data. In one use case,the local prediction of the physical divider is used to activate ordeactivate an autonomous driving mode of the vehicle and/or notify thedriver that a change in autonomous mode may be needed as previouslydescribed.

As shown, to enable this architecture 821, the physical divider platform113 trains the machine learning model 115 as previously described in themethod 500. The physical divider platform 113 can then deliver thetrained machine learning model 115 to the vehicle 103 either directly orthrough the OEM platform 801. A local system or component of the vehicle103 then executes an instance of the trained machine learning model 115to make physical divider/OPPO/VRU predictions locally at the vehicle103. In this way, the vehicle is able detect or map physical dividers onthe segments on which it is traveling when a physical divider overlay isnot available or when the vehicle does not have communications tonetwork-side components such as the physical divider platform 113 as ittravels. In one embodiment, the as new training data is collect, anupdated trained machine learning model 115 can be delivered to thevehicle 103 as needed, periodically, etc.

FIGS. 9A and 9B are diagrams of example user interfaces based onphysical dividers predicted by machine learning, according to onembodiment. In the example of FIG. 9A, the vehicle 103 is traveling on aroad segment that has not been previously mapped for the presence of anyphysical dividers between opposite traffic flow lanes. The vehicle 103also is currently operating in autonomous driving mode. As the vehicle103 approaches the segment, the vehicle sensors (e.g., camera, radar,etc.) collect sensor data. At the same time, the map data (e.g.,functional class, speed category, etc.) about the road segment is alsodetermined. A vehicle system 901 including trained machine learningmodel 115 (e.g., trained according to the embodiments described herein)then processes the map and sensor data to make a physical dividerprediction. In this example, the machine learning model 115 predictsthat there is no physical divider on the segment. This prediction thentriggers the vehicle system 901 to present a notification or an alertmessage 905 to indicate that that the vehicle is approaching an areawith no physical divider and instructs the driver to take manual controlfor the segment. In addition, the vehicle system 901 can deactivate theautonomous driving mode (e.g., following a period of time afterpresenting a notification such as the alert message 905).

FIG. 9B is a diagram illustrating an example user interface presenting aphysical divider overlay, according to one embodiment. As shown, adisplay device 921 (e.g., connected to a vehicle or personal navigationsystem) presents a mapping display on which a physical divider overlay(e.g., generated according to the various embodiments described herein)is superimposed. In this example, the physical divider overlay includesinformation on which road segments have ground truth mapped physicaldividers (e.g., segments indicated with a solid line 923) as well asphysical dividers predicted by a trained machine learning model 115(e.g., segments indicated with dashed lines 925 a and 925 b) accordingto the embodiments described herein. In addition, the physical overlay,includes data on segments with observed or predicted OPPO (e.g.,indicated by shaded area 927) as well as observed or predicted VRU(e.g., indicated by shaded are 929).

Returning to FIG. 1, in one embodiment, the physical divider platform113 has connectivity over a communication network 125 to the servicesplatform 121 (e.g., an OEM platform) that provides one or more services123 (e.g., sensor data collection services). By way of example, theservices 123 may also be other third-party services and include mappingservices, navigation services, travel planning services, notificationservices, social networking services, content (e.g., audio, video,images, etc.) provisioning services, application services, storageservices, contextual information determination services, location basedservices, information based services (e.g., weather, news, etc.), etc.In one embodiment, the services platform 121 uses the output (e.g.physical divider predictions) of the physical divider platform 113 toprovide services such as navigation, mapping, other location-basedservices, etc.

In one embodiment, the physical divider platform 113 may be a platformwith multiple interconnected components. The physical divider platform113 may include multiple servers, intelligent networking devices,computing devices, components and corresponding software for providingparametric representations of lane lines. In addition, it is noted thatthe physical divider platform 113 may be a separate entity of the system100, a part of the one or more services 123, a part of the servicesplatform 121, or included within the vehicle 103 (e.g., a physicaldivider module 119).

In one embodiment, content providers 127 a-127 m (collectively referredto as content providers 127) may provide content or data (e.g.,including geographic data, parametric representations of mappedfeatures, etc.) to the geographic database 111, the physical dividerplatform 113, the services platform 121, the services 123, and thevehicle 103. The content provided may be any type of content, such asmap content, textual content, audio content, video content, imagecontent, etc. In one embodiment, the content providers 127 may providecontent that may aid in the detecting and classifying of physicaldividers or other related characteristics (e.g., OPPO, VRU, etc.). Inone embodiment, the content providers 127 may also store contentassociated with the geographic database 111, physical divider platform113, services platform 121, services 123, and/or vehicle 103. In anotherembodiment, the content providers 127 may manage access to a centralrepository of data, and offer a consistent, standard interface to data,such as a repository of the geographic database 111.

By way of example, the physical divider module 119 can be any type ofembedded system, mobile terminal, fixed terminal, or portable terminalincluding a built-in navigation system, a personal navigation device,mobile handset, station, unit, device, multimedia computer, multimediatablet, Internet node, communicator, desktop computer, laptop computer,notebook computer, netbook computer, tablet computer, personalcommunication system (PCS) device, personal digital assistants (PDAs),audio/video player, digital camera/camcorder, positioning device,fitness device, television receiver, radio broadcast receiver,electronic book device, game device, or any combination thereof,including the accessories and peripherals of these devices, or anycombination thereof. It is also contemplated that the physical dividermodule 119 can support any type of interface to the user (such as“wearable” circuitry, etc.). In one embodiment, the physical dividermodule 119 may be associated with the vehicle 103 or be a component partof the vehicle 103.

In one embodiment, the vehicle 103 is configured with various sensorsfor generating or collecting vehicular sensor data, relatedgeographic/map data, etc. In one embodiment, the sensed data representsensor data associated with a geographic location or coordinates atwhich the sensor data was collected. In this way, the sensor data canact as observation data that can be separated into location-awaretraining and evaluation datasets according to their data collectionlocations. By way of example, the sensors may include a radar system, aLiDAR system, a global positioning sensor for gathering location data(e.g., GPS), a network detection sensor for detecting wireless signalsor receivers for different short-range communications (e.g., Bluetooth,Wi-Fi, Li-Fi, near field communication (NFC), etc.), temporalinformation sensors, a camera/imaging sensor for gathering image data,an audio recorder for gathering audio data, velocity sensors mounted onsteering wheels of the vehicles, switch sensors for determining whetherone or more vehicle switches are engaged, and the like.

Other examples of sensors of the vehicle 103 may include light sensors,orientation sensors augmented with height sensors and accelerationsensor (e.g., an accelerometer can measure acceleration and can be usedto determine orientation of the vehicle), tilt sensors to detect thedegree of incline or decline of the vehicle along a path of travel,moisture sensors, pressure sensors, etc. In a further exampleembodiment, sensors about the perimeter of the vehicle 103 may detectthe relative distance of the vehicle from a physical divider, a lane orroadway, the presence of other vehicles, pedestrians, traffic lights,potholes and any other objects, or a combination thereof. In onescenario, the sensors may detect weather data, traffic information, or acombination thereof. In one embodiment, the vehicle 103 may include GPSor other satellite-based receivers to obtain geographic coordinates fromsatellites for determining current location and time. Further, thelocation can be determined by visual odometry, triangulation systemssuch as A-GPS, Cell of Origin, or other location extrapolationtechnologies. In yet another embodiment, the sensors can determine thestatus of various control elements of the car, such as activation ofwipers, use of a brake pedal, use of an acceleration pedal, angle of thesteering wheel, activation of hazard lights, activation of head lights,etc.

In one embodiment, the communication network 125 of system 100 includesone or more networks such as a data network, a wireless network, atelephony network, or any combination thereof. It is contemplated thatthe data network may be any local area network (LAN), metropolitan areanetwork (MAN), wide area network (WAN), a public data network (e.g., theInternet), short range wireless network, or any other suitablepacket-switched network, such as a commercially owned, proprietarypacket-switched network, e.g., a proprietary cable or fiber-opticnetwork, and the like, or any combination thereof. In addition, thewireless network may be, for example, a cellular network and may employvarious technologies including enhanced data rates for global evolution(EDGE), general packet radio service (GPRS), global system for mobilecommunications (GSM), Internet protocol multimedia subsystem (IMS),universal mobile telecommunications system (UMTS), etc., as well as anyother suitable wireless medium, e.g., worldwide interoperability formicrowave access (WiMAX), Long Term Evolution (LTE) networks, codedivision multiple access (CDMA), wideband code division multiple access(WCDMA), wireless fidelity (Wi-Fi), wireless LAN (WLAN), Bluetooth®,Internet Protocol (IP) data casting, satellite, mobile ad-hoc network(MANET), and the like, or any combination thereof.

By way of example, the physical divider platform 113, services platform121, services 123, vehicle 103, and/or content providers 127 communicatewith each other and other components of the system 100 using well known,new or still developing protocols. In this context, a protocol includesa set of rules defining how the network nodes within the communicationnetwork 125 interact with each other based on information sent over thecommunication links. The protocols are effective at different layers ofoperation within each node, from generating and receiving physicalsignals of various types, to selecting a link for transferring thosesignals, to the format of information indicated by those signals, toidentifying which software application executing on a computer systemsends or receives the information. The conceptually different layers ofprotocols for exchanging information over a network are described in theOpen Systems Interconnection (OSI) Reference Model.

Communications between the network nodes are typically effected byexchanging discrete packets of data. Each packet typically comprises (1)header information associated with a particular protocol, and (2)payload information that follows the header information and containsinformation that may be processed independently of that particularprotocol. In some protocols, the packet includes (3) trailer informationfollowing the payload and indicating the end of the payload information.The header includes information such as the source of the packet, itsdestination, the length of the payload, and other properties used by theprotocol. Often, the data in the payload for the particular protocolincludes a header and payload for a different protocol associated with adifferent, higher layer of the OSI Reference Model. The header for aparticular protocol typically indicates a type for the next protocolcontained in its payload. The higher layer protocol is said to beencapsulated in the lower layer protocol. The headers included in apacket traversing multiple heterogeneous networks, such as the Internet,typically include a physical (layer 1) header, a data-link (layer 2)header, an internetwork (layer 3) header and a transport (layer 4)header, and various application (layer 5, layer 6 and layer 7) headersas defined by the OSI Reference Model.

FIG. 10 is a diagram of a geographic database, according to oneembodiment. In one embodiment, the geographic database 111 includesgeographic data 1001 used for (or configured to be compiled to be usedfor) mapping and/or navigation-related services. In one embodiment,geographic features (e.g., two-dimensional or three-dimensionalfeatures) are represented using polygons (e.g., two-dimensionalfeatures) or polygon extrusions (e.g., three-dimensional features). Forexample, the edges of the polygons correspond to the boundaries or edgesof the respective geographic feature. In the case of a building, atwo-dimensional polygon can be used to represent a footprint of thebuilding, and a three-dimensional polygon extrusion can be used torepresent the three-dimensional surfaces of the building. It iscontemplated that although various embodiments are discussed withrespect to two-dimensional polygons, it is contemplated that theembodiments are also applicable to three-dimensional polygon extrusions.Accordingly, the terms polygons and polygon extrusions as used hereincan be used interchangeably.

In one embodiment, the following terminology applies to therepresentation of geographic features in the geographic database 111.

“Node”—A point that terminates a link.

“Line segment”—A straight line connecting two points.

“Link” (or “edge”)—A contiguous, non-branching string of one or moreline segments terminating in a node at each end.

“Shape point”—A point along a link between two nodes (e.g., used toalter a shape of the link without defining new nodes).

“Oriented link”—A link that has a starting node (referred to as the“reference node”) and an ending node (referred to as the “non referencenode”).

“Simple polygon”—An interior area of an outer boundary formed by astring of oriented links that begins and ends in one node. In oneembodiment, a simple polygon does not cross itself.

“Polygon”—An area bounded by an outer boundary and none or at least oneinterior boundary (e.g., a hole or island). In one embodiment, a polygonis constructed from one outer simple polygon and none or at least oneinner simple polygon. A polygon is simple if it just consists of onesimple polygon, or complex if it has at least one inner simple polygon.

In one embodiment, the geographic database 111 follows certainconventions. For example, links do not cross themselves and do not crosseach other except at a node. Also, there are no duplicated shape points,nodes, or links. Two links that connect each other have a common node.In the geographic database 111, overlapping geographic features arerepresented by overlapping polygons. When polygons overlap, the boundaryof one polygon crosses the boundary of the other polygon. In thegeographic database 111, the location at which the boundary of onepolygon intersects they boundary of another polygon is represented by anode. In one embodiment, a node may be used to represent other locationsalong the boundary of a polygon than a location at which the boundary ofthe polygon intersects the boundary of another polygon. In oneembodiment, a shape point is not used to represent a point at which theboundary of a polygon intersects the boundary of another polygon.

As shown, the geographic database 111 includes node data records 1003,road segment or link data records 1005, POI data records 1007, physicaldivider records 1009, other records 1011, and indexes 1013, for example.More, fewer or different data records can be provided. In oneembodiment, additional data records (not shown) can include cartographic(“carto”) data records, routing data, and maneuver data. In oneembodiment, the indexes 1013 may improve the speed of data retrievaloperations in the geographic database 111. In one embodiment, theindexes 1013 may be used to quickly locate data without having to searchevery row in the geographic database 111 every time it is accessed. Forexample, in one embodiment, the indexes 1013 can be a spatial index ofthe polygon points associated with stored feature polygons.

In exemplary embodiments, the road segment data records 1005 are linksor segments representing roads, streets, or paths, as can be used in thecalculated route or recorded route information for determination of oneor more personalized routes. The node data records 1003 are end pointscorresponding to the respective links or segments of the road segmentdata records 1005. The road link data records 1005 and the node datarecords 1003 represent a road network, such as used by vehicles, cars,and/or other entities. Alternatively, the geographic database 111 cancontain path segment and node data records or other data that representpedestrian paths or areas in addition to or instead of the vehicle roadrecord data, for example.

The road/link segments and nodes can be associated with attributes, suchas geographic coordinates, street names, address ranges, speed limits,turn restrictions at intersections, and other navigation relatedattributes, as well as POIs, such as gasoline stations, hotels,restaurants, museums, stadiums, offices, automobile dealerships, autorepair shops, buildings, stores, parks, etc. The geographic database 111can include data about the POIs and their respective locations in thePOI data records 1007. The geographic database 111 can also include dataabout places, such as cities, towns, or other communities, and othergeographic features, such as bodies of water, mountain ranges, etc. Suchplace or feature data can be part of the POI data records 1007 or can beassociated with POIs or POI data records 1007 (such as a data point usedfor displaying or representing a position of a city).

In one embodiment, the geographic database 111 can also include physicaldivider records 1009 for storing predicted physical dividers, OPPO, VRU,and/or other related road characteristics. The predicted data, forinstance, can be stored as attributes or data records of a physicaldivider overlay, which fuses with the predicted attributes with mapattributes or features. In one embodiment, the physical divider records1009 can be associated with segments of a road link (as opposed to anentire link). It is noted that the segmentation of the road for thepurposes of physical divider prediction can be different than the roadlink structure of the geographic database 111. In other words, thesegments can further subdivide the links of the geographic database 111into smaller segments (e.g., of uniform lengths such as 5-meters). Inthis way, physical dividers/OPPO/VRU can be predicted and represented ata level of granularity that is independent of the granularity or atwhich the actual road or road network is represented in the geographicdatabase 111. In one embodiment, the physical divider records 1009 canbe associated with one or more of the node records 1003, road segmentrecords 1005, and/or POI data records 1007; or portions thereof (e.g.,smaller or different segments than indicated in the road segment records1005) to provide situational awareness to drivers and provide for saferautonomous operation of vehicles. In this way, the predicted physicaldividers/OPPO/VRU/etc. stored in the physical divider records 1009 canalso be associated with the characteristics or metadata of thecorresponding record 1003, 1005, and/or 1007.

In one embodiment, the geographic database 111 can be maintained by thecontent provider 127 in association with the services platform 121(e.g., a map developer). The map developer can collect geographic datato generate and enhance the geographic database 111. There can bedifferent ways used by the map developer to collect data. These ways mayinclude obtaining data from other sources, such as municipalities orrespective geographic authorities. In addition, the map developer canemploy field personnel to travel by vehicle along roads throughout thegeographic region to observe features (e.g., physical dividers, OPPO,VRU, etc.) and/or record information about them, for example. Also,remote sensing, such as aerial or satellite photography, can be used.

The geographic database 111 can be a master geographic database storedin a format that facilitates updating, maintenance, and development. Forexample, the master geographic database or data in the master geographicdatabase can be in an Oracle spatial format or other spatial format,such as for development or production purposes. The Oracle spatialformat or development/production database can be compiled into adelivery format, such as a geographic data files (GDF) format. The datain the production and/or delivery formats can be compiled or furthercompiled to form geographic database products or databases, which can beused in end user navigation devices or systems.

For example, geographic data is compiled (such as into a platformspecification format (PSF) format) to organize and/or configure the datafor performing navigation-related functions and/or services, such asroute calculation, route guidance, map display, speed calculation,distance and travel time functions, and other functions, by a navigationdevice, such as by the vehicle 103, for example. The navigation-relatedfunctions can correspond to vehicle navigation, pedestrian navigation,or other types of navigation. The compilation to produce the end userdatabases can be performed by a party or entity separate from the mapdeveloper. For example, a customer of the map developer, such as anavigation device developer or other end user device developer, canperform compilation on a received geographic database in a deliveryformat to produce one or more compiled navigation databases.

The processes described herein for providing machine learning ofphysical dividers may be advantageously implemented via software,hardware (e.g., general processor, Digital Signal Processing (DSP) chip,an Application Specific Integrated Circuit (ASIC), Field ProgrammableGate Arrays (FPGAs), etc.), firmware or a combination thereof. Suchexemplary hardware for performing the described functions is detailedbelow.

FIG. 11 illustrates a computer system 1100 upon which an embodiment ofthe invention may be implemented. Computer system 1100 is programmed(e.g., via computer program code or instructions) to provide machinelearning of physical dividers as described herein and includes acommunication mechanism such as a bus 1110 for passing informationbetween other internal and external components of the computer system1100. Information (also called data) is represented as a physicalexpression of a measurable phenomenon, typically electric voltages, butincluding, in other embodiments, such phenomena as magnetic,electromagnetic, pressure, chemical, biological, molecular, atomic,sub-atomic and quantum interactions. For example, north and southmagnetic fields, or a zero and non-zero electric voltage, represent twostates (0, 1) of a binary digit (bit). Other phenomena can representdigits of a higher base. A superposition of multiple simultaneousquantum states before measurement represents a quantum bit (qubit). Asequence of one or more digits constitutes digital data that is used torepresent a number or code for a character. In some embodiments,information called analog data is represented by a near continuum ofmeasurable values within a particular range.

A bus 1110 includes one or more parallel conductors of information sothat information is transferred quickly among devices coupled to the bus1110. One or more processors 1102 for processing information are coupledwith the bus 1110.

A processor 1102 performs a set of operations on information asspecified by computer program code related to providing machine learningof physical dividers. The computer program code is a set of instructionsor statements providing instructions for the operation of the processorand/or the computer system to perform specified functions. The code, forexample, may be written in a computer programming language that iscompiled into a native instruction set of the processor. The code mayalso be written directly using the native instruction set (e.g., machinelanguage). The set of operations include bringing information in fromthe bus 1110 and placing information on the bus 1110. The set ofoperations also typically include comparing two or more units ofinformation, shifting positions of units of information, and combiningtwo or more units of information, such as by addition or multiplicationor logical operations like OR, exclusive OR (XOR), and AND. Eachoperation of the set of operations that can be performed by theprocessor is represented to the processor by information calledinstructions, such as an operation code of one or more digits. Asequence of operations to be executed by the processor 1102, such as asequence of operation codes, constitute processor instructions, alsocalled computer system instructions or, simply, computer instructions.Processors may be implemented as mechanical, electrical, magnetic,optical, chemical or quantum components, among others, alone or incombination.

Computer system 1100 also includes a memory 1104 coupled to bus 1110.The memory 1104, such as a random access memory (RAM) or other dynamicstorage device, stores information including processor instructions forproviding machine learning of physical dividers. Dynamic memory allowsinformation stored therein to be changed by the computer system 1100.RAM allows a unit of information stored at a location called a memoryaddress to be stored and retrieved independently of information atneighboring addresses. The memory 1104 is also used by the processor1102 to store temporary values during execution of processorinstructions. The computer system 1100 also includes a read only memory(ROM) 1106 or other static storage device coupled to the bus 1110 forstoring static information, including instructions, that is not changedby the computer system 1100. Some memory is composed of volatile storagethat loses the information stored thereon when power is lost. Alsocoupled to bus 1110 is a non-volatile (persistent) storage device 1108,such as a magnetic disk, optical disk or flash card, for storinginformation, including instructions, that persists even when thecomputer system 1100 is turned off or otherwise loses power.

Information, including instructions for providing prediction of physicaldividers, is provided to the bus 1110 for use by the processor from anexternal input device 1112, such as a keyboard containing alphanumerickeys operated by a human user, or a sensor. A sensor detects conditionsin its vicinity and transforms those detections into physical expressioncompatible with the measurable phenomenon used to represent informationin computer system 1100. Other external devices coupled to bus 1110,used primarily for interacting with humans, include a display device1114, such as a cathode ray tube (CRT) or a liquid crystal display(LCD), or plasma screen or printer for presenting text or images, and apointing device 1116, such as a mouse or a trackball or cursor directionkeys, or motion sensor, for controlling a position of a small cursorimage presented on the display 1114 and issuing commands associated withgraphical elements presented on the display 1114. In some embodiments,for example, in embodiments in which the computer system 1100 performsall functions automatically without human input, one or more of externalinput device 1112, display device 1114 and pointing device 1116 isomitted.

In the illustrated embodiment, special purpose hardware, such as anapplication specific integrated circuit (ASIC) 1120, is coupled to bus1110. The special purpose hardware is configured to perform operationsnot performed by processor 1102 quickly enough for special purposes.Examples of application specific ICs include graphics accelerator cardsfor generating images for display 1114, cryptographic boards forencrypting and decrypting messages sent over a network, speechrecognition, and interfaces to special external devices, such as roboticarms and medical scanning equipment that repeatedly perform some complexsequence of operations that are more efficiently implemented inhardware.

Computer system 1100 may also include one or more instances of acommunications interface 1170 coupled to bus 1110. Communicationinterface 1170 provides a one-way or two-way communication coupling to avariety of external devices that operate with their own processors, suchas printers, scanners and external disks. In general, the coupling iswith a network link 1178 that is connected to a local network 1180 towhich a variety of external devices with their own processors areconnected. For example, communication interface 1170 may be a parallelport or a serial port or a universal serial bus (USB) port on a personalcomputer. In some embodiments, communications interface 1170 is anintegrated services digital network (ISDN) card or a digital subscriberline (DSL) card or a telephone modem that provides an informationcommunication connection to a corresponding type of telephone line. Insome embodiments, a communication interface 1170 is a cable modem thatconverts signals on bus 1110 into signals for a communication connectionover a coaxial cable or into optical signals for a communicationconnection over a fiber optic cable. As another example, communicationsinterface 1170 may be a local area network (LAN) card to provide a datacommunication connection to a compatible LAN, such as Ethernet. Wirelesslinks may also be implemented. For wireless links, the communicationsinterface 1170 sends or receives or both sends and receives electrical,acoustic or electromagnetic signals, including infrared and opticalsignals, that carry information streams, such as digital data. Forexample, in wireless handheld devices, such as mobile telephones likecell phones, the communications interface 1170 includes a radio bandelectromagnetic transmitter and receiver called a radio transceiver. Incertain embodiments, the communications interface 1170 enablesconnection to the communication network 125 for providing machinelearning of physical dividers.

The term computer-readable medium is used herein to refer to any mediumthat participates in providing information to processor 1102, includinginstructions for execution. Such a medium may take many forms,including, but not limited to, non-volatile media, volatile media andtransmission media. Non-volatile media include, for example, optical ormagnetic disks, such as storage device 1108. Volatile media include, forexample, dynamic memory 1104. Transmission media include, for example,coaxial cables, copper wire, fiber optic cables, and carrier waves thattravel through space without wires or cables, such as acoustic waves andelectromagnetic waves, including radio, optical and infrared waves.Signals include man-made transient variations in amplitude, frequency,phase, polarization or other physical properties transmitted through thetransmission media. Common forms of computer-readable media include, forexample, a floppy disk, a flexible disk, hard disk, magnetic tape, anyother magnetic medium, a CD-ROM, CDRW, DVD, any other optical medium,punch cards, paper tape, optical mark sheets, any other physical mediumwith patterns of holes or other optically recognizable indicia, a RAM, aPROM, an EPROM, a FLASH-EPROM, any other memory chip or cartridge, acarrier wave, or any other medium from which a computer can read.

FIG. 12 illustrates a chip set 1200 upon which an embodiment of theinvention may be implemented. Chip set 1200 is programmed to providemachine learning of physical dividers as described herein and includes,for instance, the processor and memory components described with respectto FIG. 11 incorporated in one or more physical packages (e.g., chips).By way of example, a physical package includes an arrangement of one ormore materials, components, and/or wires on a structural assembly (e.g.,a baseboard) to provide one or more characteristics such as physicalstrength, conservation of size, and/or limitation of electricalinteraction. It is contemplated that in certain embodiments the chip setcan be implemented in a single chip.

In one embodiment, the chip set 1200 includes a communication mechanismsuch as a bus 1201 for passing information among the components of thechip set 1200. A processor 1203 has connectivity to the bus 1201 toexecute instructions and process information stored in, for example, amemory 1205. The processor 1203 may include one or more processing coreswith each core configured to perform independently. A multi-coreprocessor enables multiprocessing within a single physical package.Examples of a multi-core processor include two, four, eight, or greaternumbers of processing cores. Alternatively or in addition, the processor1203 may include one or more microprocessors configured in tandem viathe bus 1201 to enable independent execution of instructions,pipelining, and multithreading. The processor 1203 may also beaccompanied with one or more specialized components to perform certainprocessing functions and tasks such as one or more digital signalprocessors (DSP) 1207, or one or more application-specific integratedcircuits (ASIC) 1209. A DSP 1207 typically is configured to processreal-world signals (e.g., sound) in real time independently of theprocessor 1203. Similarly, an ASIC 1209 can be configured to performedspecialized functions not easily performed by a general purposedprocessor. Other specialized components to aid in performing theinventive functions described herein include one or more fieldprogrammable gate arrays (FPGA) (not shown), one or more controllers(not shown), or one or more other special-purpose computer chips.

The processor 1203 and accompanying components have connectivity to thememory 1205 via the bus 1201. The memory 1205 includes both dynamicmemory (e.g., RAM, magnetic disk, writable optical disk, etc.) andstatic memory (e.g., ROM, CD-ROM, etc.) for storing executableinstructions that when executed perform the inventive steps describedherein to provide machine learning of physical dividers. The memory 1205also stores the data associated with or generated by the execution ofthe inventive steps.

FIG. 13 is a diagram of exemplary components of a mobile station 1301(e.g., handset, vehicle or part thereof) capable of operating in thesystem of FIG. 1, according to one embodiment. Generally, a radioreceiver is often defined in terms of front-end and back-endcharacteristics. The front-end of the receiver encompasses all of theRadio Frequency (RF) circuitry whereas the back-end encompasses all ofthe base-band processing circuitry. Pertinent internal components of thetelephone include a Main Control Unit (MCU) 1303, a Digital SignalProcessor (DSP) 1305, and a receiver/transmitter unit including amicrophone gain control unit and a speaker gain control unit. A maindisplay unit 1307 provides a display to the user in support of variousapplications and mobile station functions that offer automatic contactmatching. An audio function circuitry 1309 includes a microphone 1311and microphone amplifier that amplifies the speech signal output fromthe microphone 1311. The amplified speech signal output from themicrophone 1311 is fed to a coder/decoder (CODEC) 1313.

A radio section 1315 amplifies power and converts frequency in order tocommunicate with a base station, which is included in a mobilecommunication system, via antenna 1317. The power amplifier (PA) 1319and the transmitter/modulation circuitry are operationally responsive tothe MCU 1303, with an output from the PA 1319 coupled to the duplexer1321 or circulator or antenna switch, as known in the art. The PA 1319also couples to a battery interface and power control unit 1320.

In use, a user of mobile station 1301 speaks into the microphone 1311and his or her voice along with any detected background noise isconverted into an analog voltage. The analog voltage is then convertedinto a digital signal through the Analog to Digital Converter (ADC)1323. The control unit 1303 routes the digital signal into the DSP 1305for processing therein, such as speech encoding, channel encoding,encrypting, and interleaving. In one embodiment, the processed voicesignals are encoded, by units not separately shown, using a cellulartransmission protocol such as global evolution (EDGE), general packetradio service (GPRS), global system for mobile communications (GSM),Internet protocol multimedia subsystem (IMS), universal mobiletelecommunications system (UMTS), etc., as well as any other suitablewireless medium, e.g., microwave access (WiMAX), Long Term Evolution(LTE) networks, code division multiple access (CDMA), wireless fidelity(Wi-Fi), satellite, and the like.

The encoded signals are then routed to an equalizer 1325 forcompensation of any frequency-dependent impairments that occur duringtransmission though the air such as phase and amplitude distortion.After equalizing the bit stream, the modulator 1327 combines the signalwith a RF signal generated in the RF interface 1329. The modulator 1327generates a sine wave by way of frequency or phase modulation. In orderto prepare the signal for transmission, an up-converter 1331 combinesthe sine wave output from the modulator 1327 with another sine wavegenerated by a synthesizer 1333 to achieve the desired frequency oftransmission. The signal is then sent through a PA 1319 to increase thesignal to an appropriate power level. In practical systems, the PA 1319acts as a variable gain amplifier whose gain is controlled by the DSP1305 from information received from a network base station. The signalis then filtered within the duplexer 1321 and optionally sent to anantenna coupler 1335 to match impedances to provide maximum powertransfer. Finally, the signal is transmitted via antenna 1317 to a localbase station. An automatic gain control (AGC) can be supplied to controlthe gain of the final stages of the receiver. The signals may beforwarded from there to a remote telephone which may be another cellulartelephone, other mobile phone or a land-line connected to a PublicSwitched Telephone Network (PSTN), or other telephony networks.

Voice signals transmitted to the mobile station 1301 are received viaantenna 1317 and immediately amplified by a low noise amplifier (LNA)1337. A down-converter 1339 lowers the carrier frequency while thedemodulator 1341 strips away the RF leaving only a digital bit stream.The signal then goes through the equalizer 1325 and is processed by theDSP 1305. A Digital to Analog Converter (DAC) 1343 converts the signaland the resulting output is transmitted to the user through the speaker1345, all under control of a Main Control Unit (MCU) 1303—which can beimplemented as a Central Processing Unit (CPU) (not shown).

The MCU 1303 receives various signals including input signals from thekeyboard 1347. The keyboard 1347 and/or the MCU 1303 in combination withother user input components (e.g., the microphone 1311) comprise a userinterface circuitry for managing user input. The MCU 1303 runs a userinterface software to facilitate user control of at least some functionsof the mobile station 1301 to provide machine learning of physicaldividers. The MCU 1303 also delivers a display command and a switchcommand to the display 1307 and to the speech output switchingcontroller, respectively. Further, the MCU 1303 exchanges informationwith the DSP 1305 and can access an optionally incorporated SIM card1349 and a memory 1351. In addition, the MCU 1303 executes variouscontrol functions required of the station. The DSP 1305 may, dependingupon the implementation, perform any of a variety of conventionaldigital processing functions on the voice signals. Additionally, DSP1305 determines the background noise level of the local environment fromthe signals detected by microphone 1311 and sets the gain of microphone1311 to a level selected to compensate for the natural tendency of theuser of the mobile station 1301.

The CODEC 1313 includes the ADC 1323 and DAC 1343. The memory 1351stores various data including call incoming tone data and is capable ofstoring other data including music data received via, e.g., the globalInternet. The software module could reside in RAM memory, flash memory,registers, or any other form of writable computer-readable storagemedium known in the art including non-transitory computer-readablestorage medium. For example, the memory device 1351 may be, but notlimited to, a single memory, CD, DVD, ROM, RAM, EEPROM, optical storage,or any other non-volatile or non-transitory storage medium capable ofstoring digital data.

An optionally incorporated SIM card 1349 carries, for instance,important information, such as the cellular phone number, the carriersupplying service, subscription details, and security information. TheSIM card 1349 serves primarily to identify the mobile station 1301 on aradio network. The card 1349 also contains a memory for storing apersonal telephone number registry, text messages, and user specificmobile station settings.

While the invention has been described in connection with a number ofembodiments and implementations, the invention is not so limited butcovers various obvious modifications and equivalent arrangements, whichfall within the purview of the appended claims. Although features of theinvention are expressed in certain combinations among the claims, it iscontemplated that these features can be arranged in any combination andorder.

What is claimed is:
 1. A computer-implemented method for prediction of aphysical divider on a road, comprising: retrieving vehicular sensor dataand map data captured over a predefined period of time for at least onesegment of the road, wherein the vehicular sensor data comprisescontinuous vehicular sensor data including physical divider averagedistance, physical divider maximum distance, physical divider minimumdistance, or a combination thereof; aggregating the respective vehicularsensor data and map data for the at least one segment of the road togenerate one or more aggregated values; determining at least one of astandard deviation and a mean of each of physical divider averagedistance, physical divider maximum distance, physical divider minimumdistance, or the combination thereof; generating the one or moreaggregated values based on the determined at least one of the standarddeviation and the mean of each of physical divider average distance,physical divider maximum distance, physical divider minimum distance, orthe combination thereof; and generating, output data corresponding topresence of the physical divider in the at least one segment of theroad, by a machine learning model based on the one or more aggregatedvalues as an input to the machine learning model.
 2. The method of claim1, wherein the vehicular sensor data comprises categorical vehicularsensor data including a physical divider measurement event type, aphysical divider flag, or a combination thereof.
 3. The method of claim2 further comprising: selecting categorical values, using a votingalgorithm, for each of the physical divider measurement event type, thephysical divider flag, or the combination thereof; and generating theone or more aggregated values based on the selected categorical valuesfor each of the physical divider measurement event type, the physicaldivider flag, or the combination thereof.
 4. The method of claim 1,wherein the map data includes a functional class, a speed limit, apresence of a road sign, a bi-directionality, a number of lanes, a speedcategory, a distance to a point of interest, or a combination thereof.5. The method of claim 1, wherein the generated one or more aggregatedvalues are collected over an extended period of time for use in trainingof the machine learning model.
 6. The method of claim 5, wherein theextended period of time is larger than the predefined period of time. 7.The method of claim 1, further comprising retrieving the vehicularsensor data from one of an original equipment manufacturer (OEM) cloudor a plurality of in-vehicle sensors installed in multiple vehiclestraveling on the road.
 8. The method of claim 1, wherein the aggregatingfurther comprises uniforming the retrieved vehicular sensor data.
 9. Themethod of claim 1, further comprising notifying a user of a vehicletravelling on the at least one segment of the road regarding atransition between an autonomous driving mode of the vehicle and amanual driving mode of the vehicle based on the generated output data.10. A system for prediction of a physical divider on a road, comprising:at least one memory configured to store instructions; and at least oneprocessor configured to execute the instructions to: retrieve vehicularsensor data and map data captured over a predefined period of time forat least one segment of the road, wherein the vehicular sensor datacomprises continuous vehicular sensor data including physical divideraverage distance, physical divider maximum distance, physical dividerminimum distance, or a combination thereof; aggregate the respectivevehicular sensor data and map data for the at least one segment of theroad to generate one or more aggregated values; determine at least oneof a standard deviation and a mean of each of physical divider averagedistance, physical divider maximum distance, physical divider minimumdistance, or the combination thereof; generate the one or moreaggregated values based on the determined at least one of the standarddeviation and the mean of each of physical divider average distance,physical divider maximum distance, physical divider minimum distance, orthe combination thereof; and generate, output data corresponding topresence of the physical divider in the at least one segment of theroad, by a machine learning model based on the one or more aggregatedvalues as an input to the machine learning model.
 11. The system ofclaim 10, wherein the at least one processor is further configured toretrieve the vehicular sensor data from one of an original equipmentmanufacturer (OEM) cloud or a plurality of in-vehicle sensors installedin multiple vehicles travelling on the road.
 12. The system of claim 10,wherein the vehicular sensor data comprises categorical vehicular sensordata including a physical divider measurement event type, a physicaldivider flag, or a combination thereof, and wherein the at least oneprocessor is further configured to: select, based on a voting algorithm,categorical values for each of the physical divider measurement eventtype, the physical divider flag, or the combination thereof; andgenerate the one or more aggregated values based on the selectedcategorical values for each of the physical divider measurement eventtype, the physical divider flag, or the combination thereof.
 13. Thesystem of claim 10, wherein the generated one or more aggregated valuesare collected over an extended period of time for use in training of themachine learning model.
 14. The system of claim 10, wherein the at leastone processor is further configured to notify a user of a vehicletravelling on the at least one segment of the road regarding atransition between an autonomous driving mode of the vehicle and amanual driving mode of the vehicle, based on the generated output data.15. The system of claim 10, wherein the map data includes a functionalclass, a speed limit, a presence of a road sign, a bi-directionality, anumber of lanes, a speed category, a distance to a point of interest, ora combination thereof.
 16. A non-transitory computer-readable storagemedium having stored thereon, computer-executable instructions, whichwhen executed by a computer, cause the computer to perform operationsfor predicting a physical divider on a road, the operations comprising:retrieving vehicular sensor data, map data, or a combination thereofcaptured over a predefined period of time for at least one segment ofthe road, wherein the vehicular sensor data comprises continuousvehicular sensor data including physical divider average distance,physical divider maximum distance, physical divider minimum distance, ora combination thereof; aggregating the respective vehicular sensor data,map data, or the combination thereof for the at least one segment of theroad to generate one or more aggregated values; selectively determiningat least one of a standard deviation and a mean of each of physicaldivider average distance, physical divider maximum distance, physicaldivider minimum distance, or the combination thereof; selectivelygenerating the one or more aggregated values based on the determined atleast one of the standard deviation and the mean of each of physicaldivider average distance, physical divider maximum distance, physicaldivider minimum distance, or the combination thereof; and generating,output data corresponding to presence of the physical divider in the atleast one segment of the road, by a machine learning model based on theone or more aggregated values as an input to the machine learning model.17. The non-transitory computer-readable storage medium of claim 16,wherein the operations further comprise notifying a user of a vehicletravelling on the at least one segment of the road regarding atransition between an autonomous driving mode of the vehicle and amanual driving mode of the vehicle based on the generated output data.18. The non-transitory computer-readable storage medium of claim 16,wherein the vehicular sensor data comprises categorical vehicular sensordata including a physical divider measurement event type, a physicaldivider flag, or a combination thereof.
 19. The non-transitorycomputer-readable storage medium of claim 18, further comprising:selecting categorical values, using a voting algorithm, for each of thephysical divider measurement event type, the physical divider flag, orthe combination thereof; and generating the one or more aggregatedvalues based on the selected categorical values for each of the physicaldivider measurement event type, the physical divider flag, or thecombination thereof.
 20. The non-transitory computer-readable storagemedium of claim 16, wherein the generated one or more aggregated valuesare collected over an extended period of time for use in training of themachine learning model, and wherein the extended period of time islarger than the predefined period of time.